ABSTRACT
 
Constraints have been play an important role in data mining.  For
example, they have been used to effectively confine the search space
of frequent pattern analysis, as well as to interact with model
construction process in classification.  In this talk, I will focus
on the discussion of how constraints may influence clustering
processes in data mining.
We introduce a general framework of constraint-based clustering and
show that constraint-based clustering can be a fertile research
field.  We show that constraint-based clustering can be performed
effectively and can be used in several applications: (1) clustering
with obstructed distance, (2) privacy-preserving data mining, and
(3) user-guided clustering.  Thus we believe that constraints +
clustering = multiple interesting applications.
 
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Short bio:
Jiawei Han, Professor, Department of Computer Science, University of
Illinois at Urbana-Champaign.  He has been working on research into
data mining, data warehousing, database systems, data mining from
spatiotemporal data, multimedia data, stream and RFID data, Web
data, social network data, and biological data, with over 350
journal and conference publications. He has chaired or served on
over 100 program committees of international conferences and
workshops, including PC co-chair of 2005 (IEEE) International
Conference on Data Mining (ICDM), Americas Coordinator of 2006
International Conference on Very Large Data Bases (VLDB).  He is
also serving as the founding Editor-In-Chief of ACM Transactions on
Knowledge Discovery from Data.  He is an ACM Fellow and has received
2004 ACM SIGKDD Innovations Award and 2005 IEEE Computer Society
Technical Achievement Award. His book "Data Mining: Concepts and
Techniques" (2nd ed., Morgan Kaufmann, 2006) has been popularly used
as a textbook worldwide.